Pick one language — C++, Java, or Python — and get comfortable with its syntax first. Then follow a fixed sequence: arrays and strings, hashing, two pointers, sliding window, stacks and queues, linked lists, binary search, recursion, trees, heaps, graphs, and finally dynamic programming. Solve 2–3 easy problems daily on LeetCode or similar platforms instead of binge-watching tutorials, and maintain notes of patterns you keep forgetting. Beginners who stay consistent usually reach interview-level problem solving in 4–6 months.

A phased DSA preparation roadmap works far better than random problem solving. Spend months 1–2 on language basics, arrays, strings, and hashing; months 3–4 on linked lists, stacks, trees, heaps, and graphs with mostly medium problems; and months 5–6 on dynamic programming, advanced graph patterns, and timed contests. If you are in college, start DSA preparation for placements at least 6–8 months before the season begins so you have buffer for revision and company-specific practice. Aim for 300–400 deeply understood problems rather than 1,000 rushed ones.

Most candidates need 4–6 months of focused DSA preparation for Google, assuming 8–10 hours per week. Google's coding rounds lean heavily on medium-to-hard problems that test clean implementation and edge-case handling, so prioritise patterns like DFS/BFS, dynamic programming, binary search on answer, and graph modelling over random grinding. Around 250–300 well-understood problems, weekly timed practice, and a few mock rounds are usually enough to be interview-ready.

If you are disciplined, self-study with LeetCode and a patterns-based problem sheet is enough to crack most interviews. A structured DSA preparation course or a mentor makes sense when you keep getting stuck on the same topics, cannot judge your own readiness, or are short on time and want a curated plan with feedback. Many working professionals combine self-study with occasional mentor sessions instead of committing to a full-length course.

Start with the fundamentals: load balancing, caching, sharding, replication, message queues, CAP theorem, SQL vs NoSQL trade-offs, and consistent hashing. Then study 15–20 classic designs — URL shortener, chat app, news feed, rate limiter, notification system — and note the reusable patterns behind them. For freshers the round is lighter, but for 2+ years of experience it is often the deciding round, so practise thinking out loud about scale and trade-offs rather than memorising diagrams.

Passive reading will not prepare you — you need to design out loud under a 35–40 minute timer with a diagram in front of you. Do this weekly, rotating through classic problems, and record yourself to catch vague reasoning. The fastest improvement comes from mock interviews for FAANG rounds with a mentor or experienced peer, because honest feedback tells you whether you are going deep enough, picking the right trade-offs, and structuring the conversation the way interviewers expect.

Follow a fixed structure so you never freeze: clarify functional and non-functional requirements, do quick capacity estimates, sketch the high-level design with APIs and data flow, then deep-dive into two or three components such as the data model, database choice, and caching strategy. Close by discussing bottlenecks, failure handling, and trade-offs. Interviewers assess your reasoning and communication throughout, so keep narrating your decisions instead of silently drawing.

The classics repeat across companies: design a URL shortener, rate limiter, chat system like WhatsApp, news feed, notification system, ticket booking platform, ride-hailing app, or file storage service like Dropbox. For lower-level design rounds you may also get a parking lot, Splitwise, or an elevator system. Instead of memorising solutions, learn the underlying patterns — hashing, queues, caching, partitioning — because interviewers deliberately tweak requirements to test whether you truly understand the design.

If you want a single system design interview book to begin with, the System Design Interview by Alex Xu (Volumes 1 and 2) is the most widely recommended — it is concise, pattern-driven, and written specifically for interview rounds. Follow it with Designing Data-Intensive Applications once you want deeper distributed-systems fundamentals. Keep in mind that books build knowledge, while practising designs aloud builds the actual interview skill, so use both together.

Grokking the System Design Interview is a popular online course that teaches system design through reusable patterns and step-by-step walkthroughs of classic problems like URL shorteners and chat systems, rather than treating every problem as new. It is useful if you are starting from zero and need a structured path. Since it is largely read-based, pair it with live practice or mock discussions so you can actually communicate designs under time pressure, which is what the round really tests.

A practical FAANG interview preparation roadmap runs three tracks together: DSA first, with 8–10 weeks of pattern-focused practice; system design for anyone with 2+ years of experience; and behavioral preparation using STAR stories about impact, conflict, and failures. Add company-specific work at the end — Amazon's leadership principles, Google's coding and design depth, and so on. Most people need 3–6 months alongside a full-time job, with mock interviews concentrated in the final month.

For working professionals, FAANG interview preparation in India typically takes 4–6 months at 8–10 hours per week, mostly squeezed into evenings and weekends. Freshers and engineers switching from service-based companies often need 6–9 months because they build DSA depth and system design knowledge from scratch. Start applying only after you can consistently solve medium problems in under 30 minutes, and use referrals, which significantly improve shortlist rates in India.

FAANG interview preparation for experienced engineers puts far more weight on system design, hiring-manager rounds, and domain depth — you are evaluated on architectural judgement, trade-offs, and leadership stories, not just coding. The DSA bar still exists but usually with fewer pure-algorithm rounds than freshers face. Your resume and projects must demonstrate scale and quantified impact, because experienced candidates are screened much more strictly on relevance.

A focused resume review for software engineers is one of the highest-ROI steps before applying, because most rejections happen at the screening stage, not in the interview. A reviewer who has screened resumes at product companies catches vague bullets, missing metrics, bloated formatting, and keyword gaps that stop your profile from clearing ATS filters and the 10-second recruiter scan. Get it done before mass-applying, since a weak resume burns referrals and openings you cannot recover.

Look for mentors who currently work at, or recently interviewed for, MAANG companies and have actually sat on interview panels — they calibrate you to the real bar. You can find a mentor for MAANG interview preparation through structured mentorship platforms, specific LinkedIn outreach, or referrals from your network. A good cadence is one strategy and roadmap session upfront, followed by regular mock interviews and progress check-ins until your applications and interviews begin.